Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
- URL: http://arxiv.org/abs/2310.15330v3
- Date: Fri, 14 Jun 2024 23:03:32 GMT
- Title: Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
- Authors: Ye Tian, Haolei Weng, Yang Feng,
- Abstract summary: We introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models.
We present a comprehensive finite-sample theory that holds for general mixture models.
We then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions.
- Score: 13.857921574409362
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated EM algorithms have gained popularity in practice, however, their theoretical foundations are often lacking. In this paper, we first introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models, which supplements the existing federated EM algorithms by considering task heterogeneity and potential adversarial attacks. We present a comprehensive finite-sample theory that holds for general mixture models, then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions. Our theory elucidates when and how FedGrEM outperforms local single-task learning with insights extending to existing federated EM algorithms. This bridges the gap between their practical success and theoretical understanding. Our numerical results validate our theory, and demonstrate FedGrEM's superiority over existing unsupervised federated learning benchmarks.
Related papers
- The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm [21.36281978932632]
We consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals.
We develop a relevant theory and a practical model-free algorithm under the max-min framework.
arXiv Detail & Related papers (2024-06-12T02:47:54Z) - Every Parameter Matters: Ensuring the Convergence of Federated Learning
with Dynamic Heterogeneous Models Reduction [22.567754688492414]
Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks.
Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly.
This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
arXiv Detail & Related papers (2023-10-12T19:07:58Z) - Networked Communication for Decentralised Agents in Mean-Field Games [59.01527054553122]
We introduce networked communication to the mean-field game framework.
We show that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases.
We additionally show that the networked approach has significant advantages, over both the centralised and independent alternatives.
arXiv Detail & Related papers (2023-06-05T10:45:39Z) - Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks.
We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL.
To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
arXiv Detail & Related papers (2023-05-29T22:51:40Z) - Federated Compositional Deep AUC Maximization [58.25078060952361]
We develop a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score.
To the best of our knowledge, this is the first work to achieve such favorable theoretical results.
arXiv Detail & Related papers (2023-04-20T05:49:41Z) - Proof of Swarm Based Ensemble Learning for Federated Learning
Applications [3.2536767864585663]
In federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns.
Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications.
We propose PoSw, a novel distributed consensus algorithm for ensemble learning in a federated setting.
arXiv Detail & Related papers (2022-12-28T13:53:34Z) - Adversarial Robustness with Semi-Infinite Constrained Learning [177.42714838799924]
Deep learning to inputs perturbations has raised serious questions about its use in safety-critical domains.
We propose a hybrid Langevin Monte Carlo training approach to mitigate this issue.
We show that our approach can mitigate the trade-off between state-of-the-art performance and robust robustness.
arXiv Detail & Related papers (2021-10-29T13:30:42Z) - Fine-tuning is Fine in Federated Learning [3.222802562733787]
We study the performance of federated learning algorithms and their variants in an framework.
This multi-criterion approach naturally models the high-dimensional, many-tuned nature of federated learning.
arXiv Detail & Related papers (2021-08-16T18:59:24Z) - Tight Mutual Information Estimation With Contrastive Fenchel-Legendre
Optimization [69.07420650261649]
We introduce a novel, simple, and powerful contrastive MI estimator named as FLO.
Empirically, our FLO estimator overcomes the limitations of its predecessors and learns more efficiently.
The utility of FLO is verified using an extensive set of benchmarks, which also reveals the trade-offs in practical MI estimation.
arXiv Detail & Related papers (2021-07-02T15:20:41Z) - Federated Multi-armed Bandits with Personalization [19.85013388155711]
We propose a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning.
Under the PF-MAB framework, a mixed bandit learning problem that flexibly balances generalization and personalization is studied.
We then propose the Personalized Federated Upper Confidence Bound (PF-UCB) algorithm, where the exploration length is chosen carefully.
arXiv Detail & Related papers (2021-02-25T18:59:43Z) - FedDANE: A Federated Newton-Type Method [49.9423212899788]
Federated learning aims to jointly learn low statistical models over massively distributed datasets.
We propose FedDANE, an optimization that we adapt from DANE, to handle federated learning.
arXiv Detail & Related papers (2020-01-07T07:44:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.